Measuring the relative efficiency of Ilam hospitals using data envelopment analysis
Why this work is in the frame
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Bibliographic record
Abstract
Measuring the relative efficiency is one of the most important issues among hospitals in today's economy. These days, we hear that cost reduction is a necessity for survival of business owners and one primary to reduce the expenditures is to increase relative efficiency. The proposed study of this paper first uses output oriented data envelopment analysis (DEA) to measure the relative efficiencies of nine hospitals. The proposed model uses four types of employee namely specialists, physicians, technicians and other staffs as input parameters. The model also uses the number of surgeries, hospitalized and radiography as the outputs of the proposed model. Since the implementation of DEA leads us to have more than one single efficient unit, we implement supper efficiency technique to measure the relative efficiency of efficient units.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it